Public benchmarks play an essential role in the evaluation of large language models.

However, these methods typically rely on strict assumptions and are limited to certain types of label noise. In this paper, we reformulate the label-noise problem from a generative-model perspective, i.e., labels are generated by gradually refining an initial random guess.

These results are then generalized to general models and likelihoods, where the same claims hold.

Specifically, the latter two graphs are first disentangled from the interaction graph.